Two problems concerning the use of Ancient DNA

Last week I wrote a piece for The Wine Gourd blog, called The role of Wine Influencers — more of the same. I discussed the modern concern in the wine industry with social media Influencers, who use Facebook, Instagram, Twitter, Youtube, etc to promote wine — when LeBron James drinks a wine it will sell a whole lot better (presumably on the principle that “You may not be able to play like LeBron, but you can drink like him”).

My conclusion was that the wine industry has always had what are now called Micro- or Nano-Influencers, involving endorsements from people and organizations who possess an expert level of knowledge as well as social influence. For example, professional wine critics have always fitted this bill, notably Robert M. Parker Jr.

So, the existence of social media Wine Influencers is nothing new — it is simply the modern equivalent of something old.

Well, my blog post here is about the same idea in Ancient-DNA phylogenetics — the idea that, in spite of the claim that modern techniques provide new advantages, we may in fact simply be repeating ourselves. Modern issues are simply modern versions of the same old issues.

First problem

The first issue that I would like to raise is that of molecular data. This is seen as the crucial element of modern studies of ancient remains. Even the recent re-creation of the vineyard of Leonardo da Vinci (La Vigna di Leonardo, in Milan) involved the finding of sufficient DNA from the vineyard land, which was bombed during World War II, to identify the grape cultivar that was grown by da Vinci (Inside Leonardo da Vinci's vineyard).

The issue is that DNA studies, based on direct studies of genotype, are subject to all of the same data-analysis issues as are studies of phenotype (such as morphology, anatomy and ultrastructure).

One classic example is the supposed discovery in the 1980s of the phenomenon of Long-branch Attraction (LBA) in molecular studies. Here, if many shared nucleotide changes occur on distantly related branches of a phylogenetic tree, these branches may actually be reconstructed as sister lineages during the phylogenetic analysis. However, this is simply an example of parallelism, a phenomenon that had previously been known for decades in phylogenetic analyses of phenotype.

Many currently recognized practical problems in genotype studies, such as LBA and compositional biases, are merely specific examples of how analogy appears in molecular biology. Analogy will create convergences and parallelisms, and these will confound the attempt to detect homology.

So, reconstructing evolutionary history using molecular biology is a priori neither better nor worse than using any other source of data, because the same limitations apply. It is simply another type of data.

Second problem

The second issue that I would like to raise is that genome data are a type of Big Data, and the idea that Big Data will apparently solve all ills with data analyses. The idea seems to be that, if you can collect enough data, then you must be lead to "the truth".

This is nonsense — data are just numbers, and numbers can mislead, no matter how many there are. Data need to be interpreted by a human mind, if they are to tell that mind anything useful. The only thing that changes with the use of Big Data is the order in which the steps of the data analysis and interperation occur.

In the Old Days (ie. when I was a student), what we did was:

1. develop an experimental question
2. think about potential problems
3. collect targeted data
4. analyze the data
5. interpret the data, to answer the question.

These days, with Big Data, what people do is:

1. collect a very large amount of data
2. analyze the data, and try to interpret it
3. think of a question that the data might answer
4. discover the potential problems later.

All that is really different is the order, along with which steps are confounded with which other steps.

I don't see that this is necessarily any better; it is just different. So, don't pin your hopes on Ancient DNA genome-scale data to solve problems with your work.

Other issues

Anyone working with Ancient DNA knows that there are oddles of other problems. Some of them are discussed for the general public by Gideon Lewis-Kraus, writing on January 17 2019 for The New York Times Magazine: Is Ancient DNA Research revealing new truths — or falling into old traps? The answer is, of course, "both".

Ockham's Razor applied, but not used: can we do DNA-scaffolding with seven characters?

One of the most interesting research areas in organismal science is the cross-road between palaeontology and neontology, which puts together a picture marrying the fossil record with molecular-based phylogenies. Unfortunately, when it comes to plant (palaeo-)phylogenetics, some people adhere to outdated analysis frameworks (sometimes with little data).

How to place a fossil?

The fossil record is crucial for neontology as it can provide age constraints (minimum ages when doing node dating) and inform us about the past distribution of a lineage. This, especially in the case of plants that can't run away from unfortunate habitat changes, can be much different than today.

The main question in this context is whether a fossil represents the stem, ie. a precursor or extinct ancient sister lineage, or the crown group, ie. a modern-day taxon (primarily modern-day genus). For instance, the oldest crown fossil gives the best-possible minimum age for the stem (root) age of a modern lineage, whereas a stem fossil can give (at best) only a rough estimate for the crown age of the next-larger taxon/clade when doing the common node dating of molecular trees (note that fossilized birth-death dating can make use of both).

There are two commonly accepted criteria to identify a crown-group fossil:

1. Apomorphy-based argues that if a fossil shows a uniquely derived character (ie. a aut- or synapomorphy sensu Hennig) or character suite diagnostic for a modern-day genus, it represents a crown-group fossil.
2. Phylogeny-based aims to place the fossil in a phylogenetic framework, the position of the fossil in the genus- or species-level tree (most commonly done) or network (rarely done but producing much less biased or flawed results) then informs what it is.

(We will focus on members of modern-day genera, since it becomes more trickier for higher-level taxa, see eg. my posts thinking about What is an angiosperm? [part1][part2][why I pondered about it].)

There a three basic options to place a fossil using a phylogenetic tree.

1. Putting up a morphological matrix, then inferring the tree. A classic but due to the nature of most morphological data sets leading to a partly wrong tree as we demonstrated in some posts here on the Genealogical World of Phylogenetic Networks (hence, such analysis should always be done in a network-based exploratory data analysis framework).
2. Putting up a mixed molecular-morphological matrix, then inferring a "total evidence" tree. This includes sophisticated approaches that use the molecular data to implement weights on the morphological traits and/or consider the age of the fossils (so-called total evidence dating approaches). Works not that bad with animal-data, provided the matrix includes a lot of morphological traits reflecting aspects of the (molecular-based) phylogeny. Doesn't work too well for plants because we usually have much fewer scorable traits, most of which are evolved convergently or in parallel. Non-trivial plant fossils love to act as rogues during phylogenetic inference.
3. Optimise the position of a fossil in a molecular-based tree, eg. using so-called "DNA scaffold approach" (usually using parsimony as optimality criterion) or the evolutionary placement algorithm implemented in RAxML (using maximum likelihood). A special form of this approach is to first map the traits on a (dated) molecular tree, and then find the position where a fossil would fit best.

Why (standard) phylogenetic tree-based approaches are tricky

Below a simple example, including three fossils of different age (and often, place) with different character suites.

Even though none of the derived traits (blue and red "1") is a synapomorphy (fide Hennig), we can assign the youngest fossil X to the lineage of genus 1A just based just based on its unique derived ('apomorphic') character suite. Its likely a crown-group fossil of clade 1, and may inform a minimum age for the most-recent common ancestor (MRCA) of the two modern-day genera of Clade 1.
Apomorphy-wise, fossils Y and Z cannot be unambiguously placed. The red trait appears to be independently obtained in both clades, and the blue trait may have been

• a synapomorphy of the whole ingroup (Clade 1+2) till some point in time, lost in the genus 2B-2C lineage, ie. a symplesiomorphy sensu Hennig, or
• a parallelism / homoiology (see also Has homoiology been neglected in phylogenetics?)

To discern between the options, we'd be well-advised to do character mapping in a probabilistic framework which require a tree with independently defined branch-lengths.

Just by using parsimony-based DNA-scaffolding, fossil X would be confirmed as crown-group fossil and member of genus 1A (being identical and different from all others) and fossil Z would end up as a stem-group fossil. Fossil Y, however, would be placed as sister to genus 2C (again, identical to each other and different from all others). Using Y in node dating, would then lead to a much too old divergence age for the crown-group age of Clade 2. In reality, what researchers do with such a seemingly too old fossil is not to use it by the book, as MRCA of Genus 2B and 2C, but to inform the MRCA of eg. genera 2A, 2B, and 2C assuming that the fossil's age and trait set indicate the 2C morphology is primitive within the clade or Y is an extinct sister lineage and the shared derived trait a convergence (parallelism).

Four characters, three homoplastic and one invariant, are surely not enough for DNA-scaffolding, but adding more and more characters has a catch. Easy to do for the modern-day taxa, for which we also have molecular data, the preservation of fossils limits adding many more traits; any trait not preserved in the fossil is effectively useless when placing it (including not-preserved traits in total evidence approach may, nonetheless, help the analysis). Which brings us to the real-world example just published in Science:

Wilf P, Nixon KC, Gandolfo MA, Cúneo RA (2019). Eocene Fagaceae from Patagonia and Gondwanan legacy in Asian rainforests. Science 364, 972. Full-text article at Science website.

Why one should not place a fossil using DNA-scaffolding with seven characters

Wilf et al. show (another) spectacularly preserved fossil from the Eocene of Patagonia. Personally, I think that just publishing and shortly describing such a beautiful fossil should be enough to get into the leading biological journals.

But Wilf et al. wanted (needed?) more and came up with the following "phylogenetic analysis" to argue that their fossil is a crown-group Castanoideae, a representative of the modern-day firmly Southeast Asian tropical-subtropical genus Castanopsis, and evidence for a "southern route to Asia hypothesis" (via Antarctica and Australia, both well-studied but devoid so far of any Fagaceae presence; despite the fact that the modern-day climate allows cultivating them as eg. source for commercially used wood).

Wilf et al's Fig. 3 and Table 1 suggest to me that the paper was not critically reviewed by anyone familiar with the molecular genetics of Fagaceae or phylogenetic methods in general — perhaps this is not needed, since the first author is well-merited and the second author a world-leading expert of botanical palaeo-cladistics. However, parsimony-based DNA-scaffolding can be tricky, even with a larger set of characters (see eg. the post on Juglandaceae using a well-done matrix), and using seven is therefore quite bold. Notably, of the seven characters, one is parsimony-uninformative and four are variable within at least one of the included OTUs.

Side note: The tree used as a backbone is outdated and not comprehensive. Plastid and nuclear-molecular data indicate that the castanoids Lithocarpus (mostly tropical SE Asia) and Chrysolepis (temperate N. America) may be sisters. However, the morphologically quite similar Notholithocarpus is not related to either of these, but is instead a close relative of the ubiquitous oaks, genus Quercus (not included in Wilf et al.'s backbone tree), especially subgenus Quercus. Furthermore, the (today Eurasian) castanoid sisterpair Castanea (temperate)-Castanopsis (tropical-subtropical) have stronger affinities to the (today and in the past) Eurasian oaks of subgenus Cerris. The Fagaceae also include three distinct monotypic relict genera, the "trigonobalanoids" Formanodendron and Trigonobalanus, SE Asia, and Colombobalanus from Columbia, South America. Using a more up-to-date instead of a 2-decade-old molecular hypothesis would have been a fair request during review, as would compiling a new molecular matrix to infer a tree used as backbone (currently gene banks include > 238,000 nucleotide DNA accessions including complete plastomes). This would have also enabled the authors to map their traits using a probabilistic framework, which can protect to some degree against homoplastic bias but requires a backbone tree with defined branch-lengths.

There are many more problems with the paper and its conclusions, but this critique would be content- not network-related. Let's just look at the data and see why Wilf et al. would have better off not showing any phylogenetic analysis at all (and the impact-driven editors and positive-meaning reviewers should have advised against it). Or a network.

Clades with little character support

The scaffolding placed the Eocene fossil in a clade with both representatives of Castanopsis, from which it differs by 0–2 and 1–4 traits, respectively. Phylogeny-based, the fossil is a stem- or crown-Castanopsis.

However, the fossil has a character suite that differs in just a single trait (#6: valve deshiscence) from the (genetically very distant) sister taxon of all other Fagaceae, Fagus (the beech), used here as the outgroup to root the Castanoideae subtree. As far as apomorphies are concerned, the data are inconclusive as to whether the fossil represents a stem-Castanoideae (or extinct Fagaceae lineage) or a Castanopsis — this critical, potentially diagnostic derived trait, partial valve dehiscence, is only shared by the fossil and some but not all modern-day Castanopsis. This particular trait is not mentioned elsewhere in the text, although it is the reason the fossil is placed next to Castanopsis and not the outgroup Fagus in the "phylogenetic analysis".

In the following figure, I have mapped (with parsimony) the putative character mutations on the tree used by Wilf et al.

Black font: shared by Fagus (outgroup) and "Castanoideae". Green font: potential uniquely derived traits. Blue font: traits reconstructed as having evolved in parallel/convergently. Red branches, clades in the used backbone tree that are at odds with currently available molecular data (the N. American relict Notholithocarpus should be sister to the Eurasian Castanea-Castanopsis).

This hardly presents a strong case of crown-group assignation. Except for partial dehiscence, even the modern-day Castanopsis have little discriminating derived traits — they are living fossils with a primitive ('plesiomorphic') character suite. Intriguingly, they are also genetically less derived than other Castanoideae and the oaks (see eg. the ITS tree in Denk & Grimm 2010).

The actual differentiation pattern

The best way to depict what the character set provides as information for placing the fossil is, of course, the Neighbor-net, as shown next.

Neighbor-net based on Wilf et al.'s seven scored morphological traits used to place the fossil. Green: the current molecular-based phylogenetic synopsis — based mostly on Oh & Manos 2008; Manos et al. 2008; Denk & Grimm 2010. I had the opportunity to get familiar with all of the then-available genetic data when harvesting all Fagaceae data from gene banks in 2012 for a talk in Bordeaux. One complication in getting an all-Fagaceae-tree is that plastids, geographically constrained, and nuclear regions tell partly different stories.

Castanopsis, including the fossil, is morphologically a paraphyletic (see also our other posts dealing with paraphyla represented as clades in trees). Note also the long edge-bundle separating the temperate Chrysolepis and chestnuts (Castanea), from their respective cold-intolerant sister genera (Lithocarpus viz Castanopsis) — derived traits have been accumulated in parallel within the "Castanoideae". The scored aspects of Fagaceae morphology are very flexible and ~50 million years is a long time, possibly leading to partial valve indehiscence (or losing it) without being part of the same generic lineage. The puzzling differentiation, and the profoundly primitive appearance of the fossil (shared with modern-day Castanopsis), may in fact be the reason the authors didn't: (i) optimize / discuss very similar, co-eval fossils from the Northern Hemisphere interpreted (and cited) as extinct genera (eg. Crepet & Nixon 1989), (ii) left out the two Fagaceae genera today occurring in South America, (iii) opted for classic parsimony and a partly outdated molecular hypothesis, and (iv) just showed a naked cladogram without branch support values as the result of their "phylogenetic analysis" (Please stop using cladograms!)

Based on the scored characters, the position of the fossil in the graph, and on the background of a more up-to-date molecular-based phylogenetic synopsis (the green tree in the figure above), the most parsimonious interpretation (and probably, the most likely) is that the fossil may indeed be a stem-Castanoideae, a representative of the lineage from which the Laurasian oaks evolved at least 55 million yrs ago (oldest Quercus fossil was found in SE Asia), or even represent a morphologically primitive, extinct (South) American lineage of the Fagaceae. Regarding the "southern route", Ockham's Razor would favor that they are just a South American extension of the widespread Eocene Laurasian Fagaceae / Castanoideae, since very similar fossils and castaneoid pollen is found in equally old and older sites in North America, Greenland (papers cited by Wilf et al.) and Eurasia but not Australia, New Zealand or Antarctica.

A final note: when you have so few characters to compare, you should use OTUs that are not completely ambiguous in every potentially discriminating character, as scored for the "C. fissa group" — the "Castanopsis group" has a single unambiguously defined, potentially derived trait. Using artificial bulk taxa is generally a bad idea when mapping trait evolution onto a molecular backbone tree. Instead, you should compile a representative placeholder taxa set, with as many taxa as you need (or are feasible) to represent all character combinations seen in the modern species/genera.

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Postscriptum (14/1/2020)
Relevant matrices (NEXUS-formatted) and explicit character trait maps (Why we want to map trait evolution on networks, pt.1 – Introduction, pt.2 – Topological Ambiguity) have been uploaded to figshare.

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Other cited references, with comments

Crepet WL, Nixon KC (1989) Earliest megafossil evidence of Fagaceae: phylogenetic and biogeographic implications. American Journal of Botany 76: 842–855. – introducing a Castanopsis-like infructescence interpreted to represent an extinct genus but very similar to the new Patagonian fossil in its preserved features; and co-occuring with castaneoid pollen (not reported so far for Patagonia) and foliage.
 
Denk T, Grimm GW (2010) The oaks of western Eurasia: traditional classifications and evidence from two nuclear markers. Taxon 59: 351–366. — includes an all-"Quercaceae" ITS-tree (fig. 3) and -network (fig. 4) using data of ~ 1000 ITS accessions; the nuclear-encoded ITS is so far the only comprehensively sampled gene region that gets the genera and main intra-generic lineages apart (recently confirmed and refined by NGS phylogenomic data), something wide-sampled plastid barcodes struggle with. Analysed with up-to-date methods and avoiding long-branch interference by excluding the only partially alignable Fagus, Castanopsis dissolves into a grade in the all-accessions tree and Quercus is deeply nested within the Castanoideae (as already seen in the 2001 tree used by Wilf et al. as backbone). The species-level PBC neighbor-net prefers a ciruclar arrangement in which Notholithocarpus remains a putative sister of substantially divergent and diversified Quercus, followed by Castanea-Castanopsis, and Lithocarpus, while Chrysolepis is recognized as unique.

Oh S-H, Manos PS (2008) Molecular phylogenetics and cupule evolution in Fagaceae as inferred from nuclear CRABS CLAW sequences. Taxon 57: 434–451. – Probably still the best Fagaceae tree, and surely not a bad basis for probabilistic mapping of morphological traits in the family.

Manos PS, Cannon CH, Oh S-H (2008) Phylogenetic relationships and taxonomic status of the paleoendemic Fagaceae of Western North America: recognition of a new genus, Notholithocarpus. Madroño 55: 181–190. – the tree failed to resolve the monophyly of the largest genus, the oaks, but depicts well the data reality when combining ITS with plastid data and, hence, provides a good trade-off guide tree.

Should we try to infer trees on tree-unlikely matrices?

Spermatophyte morphological matrices that combine extinct and extant taxa notoriously have low branch support, as traditionally established using non-parametric bootstrapping under parsimony as optimality criterion. Coiro, Chomicki & Doyle (2017) recently published a pre-print to show that this can be overcome to some degree by changing to Bayesian-inferred posterior probabilities. They also highlight the use of support consensus networks for investigating potential conflict in the data. This is a good start for a scientific community that so far has put more of their trust in either (i) direct visual comparison of fossils with extant taxa or (ii) collections of most parsimonious trees inferred based on matrices with high level of probably homoplasious characters and low compatibility. But do those matrices really require or support a tree? Here, I try to answer this question.

Background

Coiro et al. mainly rely on a recent matrix by Rothwell & Stockey (2016), which marks the current endpoint of a long history of putting up and re-scoring morphology-based matrices (Coiro et al.’s fig. 1b). All of these matrices provide, to various degrees, ambiguous signal. This is not overly surprising, as these matrices include a relatively high number of fossil taxa with many data gaps (due to preservation and scoring problems), and combine taxa that perished a hundred or more millions years ago with highly derived, possibly distant-related modern counterparts.

Rothwell & Stockey state (p. 929) "As is characteristic for the results from the analysis of matrices with low character state/taxon ratios, results of the bootstrap analysis (1000 replicates) yielded a much less fully resolved tree (not figured)." Coiro et al.’s consensus trees and network based on 10,000 parsimony bootstrap replicates nicely depicts this issue, and may explain why Rothwell & Stockey decided against showing those results. When studying an earlier version of their matrix (Rothwell, Crepet & Stockey 2009), they did not provide any support values, citing a paper published in 2006, where the authors state (Rothwell & Nixon 2006, p. 739): “… support values, whether low or high for particular groups, would only mislead the reader into believing we are presenting a proposed phylogeny for the groups in question. Differences among most-parsimonious trees are sufficient to illuminate the points we wish to make here, and support values only provide what we consider to be a false sense of accuracy in these assessments”.

Do the data support a tree?

The problem is not just low support. In fact, the tree showed by Rothwell & Stockey with its “pectinate arrangement” conflicts in parts with the best-supported topology, a problem that also applied to its 2009 predecessor. This general “pectinate” arrangement of a large, low or unsupported grade is not uncommon for strict consensus trees based on morphological matrices that include fossils and extant taxa (see e.g. the more proximal parts of the Tree of Life, e.g. birds and their dinosaur ancestors).

The support patterns indicate that some of the characters are compatible with the tree, but many others are not. Of the 34 internodes (branches) in the shown tree (their fig. 28 shows a strict consensus tree based on a collection of equally parsimonious trees), 12 have lower bootstrap support under parsimony than their competing alternatives (Fig. 1). Support may be generally low for any alternative, but the ones in the tree can be among the worst.

The main problem is that the matrix simply does not provide enough tree-like signal to infer a tree. Delta Values (Holland et al. 2002) can be used as a quick estimate for the treelikeliness of signal in a matrix. In the case of large all-spermatophyte matrices (Hilton & Bateman 2006; Friis et al. 2007; Rothwell, Crepet & Stockey 2009; Crepet & Stevenson 2010), the matrix Delta Values (mDV) are ≥ 0.3. For comparison, molecular matrices resulting in more or less resolved trees have mDV of ≤ 0.15. The individual Delta Values (iDV), which can be an indicator of how well a taxon behaves during tree inference, go down to 0.25 for extant angiosperms – very distinct from all other taxa in the all-spermatophyte matrices with low proportions of missing data/gaps – and reach values of 0.35 for fossil taxa with long-debated affinities.

The newest 2016 matrix is no exception with a mDV of 0.322 (the highest of all mentioned matrices), and iDVs range between 0.26 (monocots and other extant angiosperms) and 0.39 for Doylea mongolica (a fossil with very few scored characters). In the original tree, Doylea (represented by two taxa) is part of the large grade and indicated as the sister to Gnetidae (or Gnetales) + angiosperms (molecular trees associate the Gnetidae with conifers and Ginkgo). According to the bootstrap analysis, Doylea is closest to the extant Pinales, the modern conifers. Coiro et al. found the same using Bayesian inference. Their posterior probability (PP) of a Doylea-Podocarpus-Pinus clade is 0.54, and Rothwell & Stockey’s Doylea-Ginkgo-angiosperm clade conflicts with a series of splits with PPs up to 0.95.

Figure 1. Parsimony bootstrap network based on 10,000 pseudoreplicate trees
inferred from the matrix of Rothwell & Stockey.
Edges not found in the authors’ tree in red, edges also found in the tree in green.
Extant taxa in blue bold font. The edge length is proportional to the frequency of the
according split (taxon bipartition, branch in a possible tree) in the pseudoreplicate
tree sample. The network includes all edges of the authors’ tree except for
Doylea + Gnetidae + Petriellales + angiosperms vs. all other gymnosperms and
extinct seed plant groups. Such a split has also no bootstrap support (BS < 10)
using least-square and maximum likelihood optimum criteria.

Do the data require a tree?

As David made a point in an earlier post, neighbour-nets are not really “phylogenetic networks” in the evolutionary sense. Being unrooted and 2-dimensional, they don’t depict a phylogeny, which has to be a sort of (rooted) tree, a one-dimensional graph with time as the only axis (this includes reticulation networks where nodes can be the crossing point of two internodes rather than their divergence point). The neighbour-net algorithm is an extension into two dimensions of the neighbour-joining algorithm, the latter infers a phylogenetic tree serving a distance criterion such as minimum evolution or least-squares (Felsenstein 2004). Essentially, the neighbour-net is a ‘meta-phylogenetic’ graph inferring and depicting the best and second-best alternative for each relationship. Thus, neighbour-nets can help to establish whether the signal from a matrix, treelike or not as it is the cases here, supports potential and phylogenetic relationships, and explore the alternatives much more comprehensively than would be possible with a strict-consensus or other tree (Fig. 2).

Figure 2. Neighbour-net based on a mean distance matrix inferred
from the matrix of Rothwell & Stockey.
The distance to the "progymnosperms", a potential ancestral group of the
seed plants, can be taken as a measurement for the derivedness of each
major group. The primitive seed ferns are placed between progymnosperms
 and the gymnosperms connected by partly compatible edge bundles; the
putatively derived "higher seed ferns" isolated between the progymnosperms
and the long-edged angiosperms. Shared edge-bundles and 'neighbourness'
reflect quite well potential phylogenetic relationships and eventual ambiguities,
as in the case of Gnetidae. Colouring as in Figure 1; some taxon names
are abbreviated.

In addition, neighbour-nets usually are better backgrounds to map patterns of conflicting or partly conflicting support seen in a bootstrap, jackknife or Bayesian-inferred tree sample. In Fig. 3, I have mapped the bootstrap support for alternative taxon bipartitions (branches in a tree) on the background of the neighbour-net in Fig. 2.

Obvious and less-obvious relationships are simultaneously revealed, and their competing support patterns depicted. Based on the graph, we can see (edge lengths of the neighbour-net) that there is a relatively weak primary but substantial bootstrap support for the Petriellales (a recently described taxon new to the matrix) as sister to the angiosperms. Several taxa, or groups of closely related taxa, are characterised by long terminal edges/edge bundles, rooting in the boxy central part of the graph. Any alternative relationship of these taxa/taxon groups receives equally low support, but there are notable differences in the actual values.

There is little signal to place most of the fossil “seed ferns” (extinct seed plants) in relation to the modern groups, and a very ambiguous signal regarding the relationship of the Gnetidae (or Gnetales) with the two main groups of extant seed plants, the conifers (Pinidae; see C. Earle’s gymnosperm database) and angiosperms (for a list and trees, see P. Stevens’ Angiosperm Phylogeny Website).

The Gnetidae is a strongly distinct (also genetically) group of three surviving genera, being a persistent source of headaches for plant phylogeneticists. Placed as sister to the Pinaceae (‘Gnepine’ hypothesis) in early molecular trees (long-branch attraction artefact), the currently favoured hypothesis (‘Gnetifer’) places the Gnetidae as sister to all conifers (Pinatidae) in an all-gymnosperm clade (including Gingko and possibly the cycads).

As favoured by the branch support analyses, and contrasting with the preferred 2016 tree, the two Doyleas are placed closest to the conifers, nested within a commonly found group including the modern and ancient conifers and their long-extinct relatives (Cordaitales), and possibly Ginkgo (Ginkgoidae). In the original parsimony strict consensus tree, they are placed in the distal part as sister to a Gnetidae and Petriellales + angiosperms (possibly long-branch attraction). The grade including the ‘primitive seed ferns’ (Elkinsia through Callistophyton), seen also in Rothwell and Stockey’s 2016 tree, may be poorly supported under maximum parsimony (the criterion used to generate the tree), but receives quite high support when using a probabilistic approach such as maximum likelihood bootstrapping or Bayesian inference to some degree (Fig. 3; Coiro, Chomicki & Doyle 2017).

Figure 3. Neighbour-net from above used to map alternative support patterns.
Numbers refer to non-parametric bootstrap (BS) support for alternative phylogenetic
splits under three optimality criteria: maximum likelihood (ML) as implemented in
RAxML (using MK+G model), maximum parsimony (MP), and least-squares
(via neighbour-joining, NJ; using PAUP*); and Bayesian posterior probabilties
(using MrBayes 3.2; see Denk & Grimm 2009, for analysis set-up). The circular
arrangement of the taxa allows tracking most edges in the authors’ tree and their,
sometimes better supported, alternatives. The edge lengths provide direct
information about the distinctness of the included taxa to each other; the structure
of the graph informs about the how tree-like the signal is regarding possible
phylogenetic relationships or their alternatives. Colouring as in Figure 1;
some taxon names are abbreviated.

Numerous morphological matrices provide non-treelike signals. A tree can be inferred, but its topology may be only one of many possible trees. In the framework of total evidence, this may be not such a big problem, because the molecular partitions will predefine a tree, and fossils will simply be placed in that tree based on their character suites. Without such data, any tree may be biased and a poor reflection of the differentiation patterns.

By not forcing the data in a series of dichotomies, neighbour-nets provide a quick, simple alternative. Unambiguous, well-supported branches in a tree will usually result in tree-like portions of the neighbour net. Boxy portions in the neighbour-net pinpoint the ambiguous or even problematic signals from the matrix. Based on the graph, one can extract the alternatives worth testing or exploring. Support for the alternatives can be established using traditional branch support measures. Since any morphological matrix will combine those characters that are in line with the phylogeny as well as those that are at odds with it (convergences, character misinterpretations), the focus cannot be to infer a tree, but to establish the alternative scenarios and the support for them in the data matrix.

References

Coiro M, Chomicki G, Doyle JA. 2017. Experimental signal dissection and method sensitivity analyses reaffirm the potential of fossils and morphology in the resolution of seed plant phylogeny. bioRxiv DOI:10.1101/134262

Crepet WL, Stevenson DM. 2010. The Bennettitales (Cycadeoidales): a preliminary perspective of this arguably enigmatic group. In: Gee CT, ed. Plants in Mesozoic Time: Morphological Innovations, Phylogeny, Ecosystems. Bloomington: Indiana University Press, pp. 215-244.

Denk T, Grimm GW. 2009. The biogeographic history of beech trees. Review of Palaeobotany and Palynology 158: 83-100.

Felsenstein J. 2004. Inferring Phylogenies. Sunderland, MA, U.S.A.: Sinauer Associates Inc.

Friis EM, Crane PR, Pedersen KR, Bengtson S, Donoghue PCJ, Grimm GW, Stampanoni M. 2007. Phase-contrast X-ray microtomography links Cretaceous seeds with Gnetales and Bennettitales. Nature 450: 549-552 [all important information needed for this post is in the supplement to the paper; a figure showing the actual full analysis results can be found at figshare]

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Rothwell GW, Crepet WL, Stockey RA. 2009. Is the anthophyte hypothesis alive and well? New evidence from the reproductive structures of Bennettitales. American Journal of Botany 96: 296–322.

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Similarities and language relationship

There is a long-standing debate in linguistics regarding the best proof deep relationships between languages. Scholars often break it down to the question of words vs. rules, or lexicon vs. grammar. However, this is essentially misleading, since it suggests that only one type of evidence could ever be used, whereas most of the time it is the accumulation of multiple pieces of evidence that helps to convince scholars. Even if this debate is misleading, it is interesting, since it reflects a general problem of historical linguistics: the problem of similarities between languages, and how to interpret them.

Unlike (or like?) biology, linguistics has a serious problem with similarities. Languages can be strikingly similar in various ways. They can share similar words, but also similar structures, similar ways of expressing things.

In Chinese, for example, new words can be easily created by compounding existing ones, and the word for 'train' is expressed by combining huǒ 火 'fire' and chē 車 'wagon'. The same can be done in languages like German and English, where the words Feuerwagen and fire wagon will be slightly differently interpreted by the speakers, but the constructions are nevertheless valid candidates for words in both languages. In Russian, on the other hand, it is not possible to just put two nouns together to form a new word, but one needs to say something as огненная машина (ognyonnaya mašína), which literally could be translated as 'firy wagon'.

Neither German nor English are historically closely related to Chinese, but German, English, and Russian go back to the same relatively recent ancestral language. We can see that whether a language allows compounding of two words to form a new one or not, is not really indicative of its history, as is the question of whether a language has an article, or whether it has a case system.

The problem with similarities between languages is that the apparent similarities may have different sources, and not all of them are due to historical development. Similarities can be:

1. coincidental (simply due to chance),
2. natural (being grounded in human cognition),
3. genealogical (due to common inheritance), and
4. contact-induced (due to lateral transfer).

As an example for the first type of similarity, consider the Modern Greek word θεός [θɛɔs] ‘god’ and the Spanish dios [diɔs] ‘god’. Both words look similar and sound similar, but this is a sheer coincidence. This becomes clear when comparing the oldest ancestor forms of the words that are reflected in written sources, namely Old Latin deivos, and Mycenaean Greek thehós (Meier-Brügger 2002: 57f).

As an example of the second type of similarity, consider the Chinese word māmā 媽媽 'mother' vs. the German Mama 'mother'. Both words are strikingly similar, not because they are related, but because they reflect the process of language acquisition by children, which usually starts with vowels like [a] and the nasal consonant [m] (Jakobson 1960).

An example of genealogical similarity is the German Zahn and the English tooth, both going back to a Proto-Germanic form *tanθ-. Contact-induced similarity (the fourth type) is reflected in the English mountain and the French montagne, since the former was borrowed from the latter.

We can display these similarities in the following decision tree, along with examples from the lexicon of different languages (see List 2014: 56):

Four basic types of similarity in linguistics

In this figure, I have highlighted the last two types of similarity (in a box) in order to indicate that they are historical similarities. They reflect individual language development, and allow us to investigate the evolutionary history of languages. Natural and coincidental similarities, on the other hand, are not indicative of history.

When trying to infer the evolutionary history of languages, it is thus crucial to first rule out the non-historical similarities, and then the contact-induced similarities. The non-historical similarities will only add noise to the historical signal, and the contact-induced similarities need to be separated from the genealogical similarities, in order to find out which languages share a common origin and which languages have merely influenced each other some time during their history.

Unfortunately, it is not trivial to disentangle these similarities. Coincidence, for example, seems to be easy to handle, but it is notoriously difficult to calculate the likelihood of chance similarities. Scholars have tried to model the probability of chance similarities mathematically, but their models are far too simple to provide us with good estimations, as they usually only consider the first consonant of a word in no more than 200 words of each language (Ringe 1992, Baxter and Manaster Ramer 2000, Kessler 2001).

The problem here is that everything that goes beyond word-initial consonants would have to take the probability of word structures into account. However, since languages differ greatly regarding their so-called phonotactic structure (that is, the sound combinations they allow to occur inside a syllable or a word), an account on chance similarities would need to include a probabilistic model of possible and language-specific word structures. So far, I am not aware of anybody who has tried to tackle this problem.

Even more problematic is the second type of similarity. At first sight, it seems that one could capture natural similarities by searching for similarities that recur in very diverse locations of the world. If we compare, for example, which languages have tones, and we find that tones occur almost all over the world, we could argue that the existence of tone languages is not a good indicator of relatedness, since tonal systems can easily develop independently.

The problem with independent development, however, is again tricky, as we need to distinguish different aspects of independence. Independent development could be due to: human cognition (the fact that many languages all over the world denote the bark of a tree with a compound tree-skin is obviously grounded in our perception); or due to language acquisition (like the case of words for 'mother'); but potentially also due to environmental factors, such as the size of the population of speakers (Lupyan et al. 2010), or the location where the languages are spoken (see Everett et al. 2015, but also compare the critical assessment in Hammarström 2016).

Convergence (in linguistics, the term is used to denote similar development due to contact) is a very frequent phenomenon in language evolution, and can happen in all domains of language. Often we simply do not know enough to make a qualified assessment as to whether certain features that are similar among languages are inherited/borrowed or have developed independently.

Interestingly, this was first emphasized by Karl Brugmann (1849-1919), who is often credited as the "father of cladistic thinking" in linguistics. Linguists usually quote his paper from 1884, in order to emphasize the crucial role that Brugmann attributed to shared innovations (synapomorphies in the cladistic terminology) for the purpose of subgrouping. When reading this paper thoroughly, however, it is obvious that Brugmann himself was much less obsessed with the obscure and circular notion of shared innovations (which also holds for cladistics in biology; see De Laet 2005), but with the fact that it is often impossible to actually find them, due to our incapacity to disentangle independent development, inheritance and borrowing.

So far, most linguistic research has concentrated on the problem of distinguishing borrowed from inherited traits, and it is here that the fight over lexicon or grammar as primary evidence for relatedness primarily developed. Since certain aspects of grammar, like case inflection, are rarely transferred from one language to another, while words are easily borrowed, some linguists claim that only grammatical similarities are sufficient evidence of language relationship. This argument is not necessarily productive, since many languages simply lack grammatical structures like inflection, and will therefore not be amenable to any investigation, if we only accept inflectional morphology (grammar) as rigorous proof (for a full discussion, see Dybo and Starostin 2008). Luckily, we do not need to go that far. Aikhenvald (2007: 5) proposes the following borrowability scale:

Aikhenvald's (2007) scale of borrowability

As we can see from this scale, core lexicon (basic vocabulary) ranks second, right behind inflectional morphology. Pragmatically, we can thus say: if we have nothing but the words, it is better to compare words than anything else. Even more important is that, even if we compare what people label "grammar", we compare concrete form-meaning pairs (e.g., concrete plural-endings), and we never compare abstract features (e.g., whether languages have an article). We do so in order to avoid the "homoplasy problem" that causes so many headaches in our research. No biologist would group insects, birds, and bats based on their wings; and no linguist would group Chinese and English due to their lack of complex morphology and their preference for compound words.

Why do I mention all this in this blog post? For three main reasons. First, the problem of similarity is still creating a lot of confusion in the interdisciplinary dialogues involving linguistics and biology. David is right: similarity between linguistic traits is more like similarity in morphological traits in biology (phenotype), but too often, scholars draw the analogy with genes (genotype) (Morrison 2014).

Second, the problem of disentangling different kinds of similarities is not unique to linguistics, but is also present in biology (Gordon and Notar 2015), and comparing the problems that both disciplines face is interesting and may even be inspiring.

Third, the problem of similarities has direct implications for our null hypothesis when considering certain types of data. David asked in a recent blog post: "What is the null hypothesis for a phylogeny?" When dealing with observed similarity patterns across different languages, and recalling that we do not have the luxury to assume monogenesis in language evolution, we might want to know what the null hypothesis for these data should be. I have to admit, however, that I really don't know the answer.

References

• Aikhenvald, A. (2007): Grammars in contact. A cross-linguistic perspective. In: Aikhenvald, A. and R. Dixon (eds.): Grammars in Contact. Oxford University Press: Oxford. 1-66.
• Baxter, W. and A. Manaster Ramer (2000): Beyond lumping and splitting: Probabilistic issues in historical linguistics. In: Renfrew, C., A. McMahon, and L. Trask (eds.): Time depth in historical linguistics. McDonald Institute for Archaeological Research: Cambridge. 167-188.
• Brugmann, K. (1884): Zur Frage nach den Verwandtschaftsverhältnissen der indogermanischen Sprachen [Questions regarding the closer relationship of the Indo-European languages]. Internationale Zeischrift für allgemeine Sprachewissenschaft 1. 228-256.
• De Laet, J. (2005): Parsimony and the problem of inapplicables in sequence data. In: Albert, V. (ed.): Parsimony, phylogeny, and genomics. Oxford University Press: Oxford. 81-116.
• Dybo, A. and G. Starostin (2008): In defense of the comparative method, or the end of the Vovin controversy. In: Smirnov, I. (ed.): Aspekty komparativistiki.3. RGGU: Moscow. 119-258.
• Everett, C., D. Blasi, and S. Roberts (2015): Climate, vocal folds, and tonal languages: Connecting the physiological and geographic dots. Proceedings of the National Academy of Sciences 112.5. 1322-1327.
• Gordon, M. and J. Notar (2015): Can systems biology help to separate evolutionary analogies (convergent homoplasies) from homologies?. Progress in Biophysics and Molecular Biology 117. 19-29.
• Hammarström, H. (2016): There is no demonstrable effect of desiccation. Journal of Language Evolution 1.1. 65–69.
• Jakobson, R. (1960): Why ‘Mama’ and ‘Papa’?. In: Perspectives in psychological theory: Essays in honor of Heinz Werner. 124-134.
• Kessler, B. (2001): The significance of word lists. Statistical tests for investigating historical connections between languages. CSLI Publications: Stanford.
• List, J.-M. (2014): Sequence comparison in historical linguistics. Düsseldorf University Press: Düsseldorf.
• Lupyan, G. and R. Dale (2010): Language structure is partly determined by social structure. PLoS ONE 5.1. e8559.
• Meier-Brügger, M. (2002): Indogermanische Sprachwissenschaft. de Gruyter: Berlin and New York.
• Morrison, D. (2014): Is the Tree of Life the best metaphor, model, or heuristic for phylogenetics?. Systematic Biology 63.4. 628-638.
• Ringe, D. (1992): On calculating the factor of chance in language comparison. Transactions of the American Philosophical Society 82.1. 1-110.